183 lines
8.1 KiB
C++
183 lines
8.1 KiB
C++
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// Created by raver119 on 29/10/17.
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_fused_batch_norm)
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#include <ops/declarable/CustomOperations.h>
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namespace sd {
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namespace ops {
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DECLARE_TYPES(fused_batch_norm) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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CUSTOM_OP_IMPL(fused_batch_norm, 3, 3, false, 0, 2) {
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auto x = INPUT_VARIABLE(0); // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
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auto scale = INPUT_VARIABLE(1); // [iD]
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auto offset = INPUT_VARIABLE(2); // [iD]
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auto y = OUTPUT_VARIABLE(0); // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
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auto batchMean = OUTPUT_VARIABLE(1); // [iD]
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auto batchVar = OUTPUT_VARIABLE(2); // [iD]
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const bool dataFormat = (bool)INT_ARG(0); // 0->NHWC, 1->NCHW
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const bool isTraining = (bool)INT_ARG(1);
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nd4j_debug("CUSTOM_OP fused_batch_norm: data format, is NCHW: %d, isTraining: %d\n",dataFormat,isTraining);
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REQUIRE_TRUE(x->rankOf() == 4, 0, "CUSTOM_OP fused_batch_norm: the rank of input x array must be equal to 4, but got %i instead !", x->rankOf());
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int bS = x->sizeAt(0); // batch size
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int iH, iW, iD; // input height, input width, input depth(number of channels)
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if(dataFormat) {
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iD = x->sizeAt(1);
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iH = x->sizeAt(2);
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iW = x->sizeAt(3);
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}
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else {
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iD = x->sizeAt(3);
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iH = x->sizeAt(1);
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iW = x->sizeAt(2);
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}
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auto xCast = x->cast(sd::DataType::FLOAT32);
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//move to NWHC
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/**
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* TODO: TF has a permute to NWHC here:
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* https://github.com/tensorflow/tensorflow/blob/ce34a83e03394492b1c4e5bb92fbd56da2ba7ce5/tensorflow/core/kernels/fused_batch_norm_op.cc#L137
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*
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* This should be done as well for us, but results are still off.
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* Figure out differences.
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*/
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if(dataFormat) {
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xCast.printShapeInfo("x cast shape info pre permute");
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xCast = xCast.permute({0, 2, 3, 1});
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xCast.printShapeInfo("x cast shape info post permute");
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}
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REQUIRE_TRUE(scale->rankOf() == 1 && scale->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scale).c_str());
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REQUIRE_TRUE(offset->rankOf() == 1 && offset->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input offset array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(offset).c_str());
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NDArray *mean(nullptr), *variance(nullptr);
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if(!isTraining) {
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mean = INPUT_VARIABLE(3);
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variance = INPUT_VARIABLE(4);
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REQUIRE_TRUE(mean->rankOf() == 1 && mean->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input mean array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(mean).c_str());
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REQUIRE_TRUE(variance->rankOf() == 1 && variance->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input variance array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(variance).c_str());
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}
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else {
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//REQUIRE_TRUE(block.width() == 3, 0, "CUSTOM_OP fused_batch_norm: when isTraining=true then number of input arrays must be equal to 3, but got %i instead !", block.width());
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std::vector<Nd4jLong> shape = {iD};
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mean = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
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variance = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
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}
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float epsilon;
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if(block.getTArguments()->size() > 0) {
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epsilon = (float) (T_ARG(0) > 1.001e-5 ? T_ARG(0) : 1.001e-5);
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}
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else {
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epsilon = 0.001f;
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}
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const int restSize = x->lengthOf() / iD;
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auto xAffected = NDArrayFactory::create(x->ordering(), {restSize, iD}, sd::DataType::FLOAT32, block.launchContext());
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xAffected.assign(xCast);
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const int restSizeMinusOne = (restSize > 1) ? (restSize - 1) : 1;
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const float restSizeInv = 1.0f / restSize;
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const float restSizeAdjust = (float)restSize / restSizeMinusOne;
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if(isTraining) {
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auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
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sum *= restSizeInv;
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mean->assign(sum);
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*batchMean = *mean;
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}
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else
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*batchMean = 0.;
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auto xCentered = xAffected - *mean;
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xAffected -= *mean;
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if(isTraining) {
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int power = 2;
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xAffected.applyScalar(scalar::Pow, power, xAffected);
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auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
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sum *= restSizeInv;
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variance->assign(sum);
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auto varOutput = (*variance) * restSizeAdjust;
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batchVar->assign(varOutput);
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}
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else
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*batchVar = 0.;
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auto scaledVariance = ((*variance + epsilon).transform(transform::RSqrt) * (*scale)).cast(xAffected.dataType());
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auto xScaled1 = xCentered * scaledVariance;
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auto xShifted1 = xScaled1 + *offset;
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if(dataFormat) {
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//need to reshape from matrix to 4d then permute the ordering due to NWHC ordering
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auto reshaped = xShifted1.reshape(xCast.ordering(),xCast.getShapeAsVector());
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reshaped.permutei({0,3,1,2});
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y->assign(reshaped);
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}
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else //NWHC case
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y->assign(xShifted1);
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if(isTraining) {
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delete mean;
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delete variance;
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}
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return Status::OK();
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}
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DECLARE_SHAPE_FN(fused_batch_norm) {
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auto xShapeInfo = inputShape->at(0);
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auto scaleShapeInfo = inputShape->at(1);
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const bool dataFormat = (bool)INT_ARG(0); // 0->NHWC, 1->NCHW
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const int iD = dataFormat ? xShapeInfo[2] : xShapeInfo[4];
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REQUIRE_TRUE(scaleShapeInfo[0] == 1 && scaleShapeInfo[1] == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scaleShapeInfo).c_str());
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Nd4jLong* outShapeInfo(nullptr), *batchMeanShapeInfo(nullptr), *batchVarShapeInfo(nullptr);
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COPY_SHAPE(xShapeInfo, outShapeInfo);
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COPY_SHAPE(scaleShapeInfo, batchMeanShapeInfo);
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COPY_SHAPE(scaleShapeInfo, batchVarShapeInfo);
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return SHAPELIST(CONSTANT(outShapeInfo), CONSTANT(batchMeanShapeInfo), CONSTANT(batchVarShapeInfo));
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}
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}
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}
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#endif |